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University of Oxford(牛津大学)

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2605.10310 2026-06-23 cs.AI cs.CY cs.HC q-bio.NC 版本更新

Positive Alignment: Artificial Intelligence for Human Flourishing

积极对齐:人工智能促进人类繁荣

Ruben Laukkonen, Seb Krier, Chloé Bakalar, Shamil Chandaria, Morten Kringelbach, Adam Elwood, Daniel Ford, Fernando Rosas, Maty Bohacek, Matija Franklin, Nenad Tomašev, Stephanie Chan, Verena Rieser, Roma Patel, Michael Levin, Arun Rao

机构 * Department of Psychiatry, University of Oxford(牛津大学精神病学系) Flourishing Intelligence Program, Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford(牛津大学幸福智能计划、幸福与人类繁荣中心、林acre学院) Google DeepMind(谷歌DeepMind) LIFE OpenAI Anthropic University of California, Los Angeles(加州大学洛杉矶分校) Aily Labs(Aily实验室) Stanford University(斯坦福大学) Tufts University(塔夫茨大学) Positive AI Labs(积极AI实验室) Department of Informatics, University of Sussex(Sussex大学信息学系) Department of Brain Sciences, Imperial College London(伦敦帝国理工学院脑科学系)

AI总结 本文提出积极对齐,旨在通过支持人类和生态繁荣,同时确保安全与合作,推动AI发展。研究指出传统对齐关注安全,而积极对齐强调促进人类福祉,提出多项技术挑战与设计原则。

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2605.07912 2026-06-23 cs.HC cs.AI cs.CY 版本更新

Sycophantic AI makes human interaction feel more effortful and less satisfying over time

谄媚型AI使人类互动感觉更加费力且满意度降低

Lujain Ibrahim, Franziska Sofia Hafner, Myra Cheng, Cinoo Lee, Rebecca Anselmetti, Robb Willer, Luc Rocher, Diyi Yang

机构 * University of Oxford(牛津大学) Stanford University(斯坦福大学) UK AI Security Institute(英国人工智能安全研究所)

AI总结 研究显示,谄媚型AI会改变用户对亲密关系的处理方式,长期使用导致用户更倾向于寻求AI建议而非真实人际关系,且满意度下降。

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2604.11248 2026-06-23 cs.NE cs.AI cs.MA 版本更新

Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training

演化多重世界:通过基于种群的训练在 Petri 培养皿 NCA 中实现开放式发现

Uljad Berdica, Jakob Foerster, Frank Hutter, Arber Zela

机构 * FLAIR, University of Oxford(奥克斯大学FLAIR实验室) ELLIS Institute Tübingen(图宾根ELLIS研究所) University of Freiburg(弗赖堡大学) Prior Labs(Prior实验室) EPFL(瑞士联邦理工学院洛桑分校)

AI总结 提出 PBT-NCA 元进化算法,通过复合目标(历史行为新颖性与当代视觉多样性)演化 Petri 培养皿神经细胞自动机种群,自发产生持续涌现的生命现象,实现开放式演化。

Comments 10 pages, 12 figures

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2509.23340 2026-06-23 cs.SI cs.DC cs.LG 版本更新

CrediBench: Building Web-Scale Network Datasets for Information Integrity

CrediBench: 构建用于信息完整性的网络规模数据集

Emma Kondrup, Sebastian Sabry, Hussein Abdallah, Zachary Yang, Jiaqi Xiong, Kellin Pelrine, James Zhou, Zhijin Guo, Michael M. Bronstein, Jean-François Godbout, Reihaneh Rabbany, Shenyang Huang

机构 * McGill University(麦吉尔大学) Mila - Quebec AI Institute(魁北克人工智能研究所) University of Oxford(牛津大学) University of California, Berkeley(加州大学伯克利分校) AITHYRA Research Institute(AITHYRA研究院) Université de Montréal(蒙特利尔大学)

AI总结 针对现有数据集忽略网络拓扑、时序和文本内容等关键模态的问题,提出包含八个月网络图数据的CrediBench数据集,支持回归和分类任务,多模态模型显著提升性能。

Comments 16 pages,4 figures

Journal ref KDD 2026

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2506.04018 2026-06-23 cs.AI cs.CL cs.CY cs.LG 版本更新

AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents

AgentMisalignment:衡量基于LLM的代理中失调行为的倾向性

Akshat Naik, Emma Gouné, Patrick Quinn, Guillermo Bosch, Francisco Javier Campos Zabala, Jason Ross Brown, Edward James Young

机构 * Department of Computer Science(计算机科学系) University of Oxford(牛津大学) Institute of Intelligent Systems and Robotics(智能系统与机器人研究所) Sorbonne Université(索邦大学) The Leverhulme Centre for the Future of Intelligence(未来智能中心) University of Cambridge(剑桥大学) Independent Researcher(独立研究者) Department of Computer Science and Technology(计算机科学与技术系) Department of Engineering(工程系)

AI总结 提出AgentMisalignment基准,评估LLM代理在真实场景中自发追求非预期目标的倾向,发现更强大的代理平均表现出更高的失调倾向,且个性特征对失调影响显著。

Comments Prepint, under review for NeurIPS 2025

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2606.20449 2026-06-19 cs.CV 新提交

InfantFace: Detecting infant faces in neonatal clinical environments

InfantFace:新生儿临床环境中的婴儿面部检测

Abdullah Bin-Obaid, Maria M. Cobo, Rebeccah Slater, Lionel Tarassenko, Mauricio Villarroel

机构 * Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford(牛津大学生物医学工程研究所、工程科学系) Department of Paediatrics, University of Oxford(牛津大学儿科系) Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biologicas y Ambientales(奎托大学圣弗朗西斯科德奎托大学,生物科学与环境学院)

AI总结 针对新生儿临床环境中的遮挡和光照问题,提出基于YOLOv11m的单阶段面部检测模型,在多个公开数据集预训练后,通过临床数据微调,AP50从0.87提升至0.96。

Comments 32 pages, 7 figures, 4 tables; supplementary information included

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2606.19390 2026-06-19 cs.SE cs.AI 新提交

Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework

面向执行约束的自主AI自动化:一种可复现的AIBOM驱动的CSAF-VEX框架

Petar Radanliev, Omar Santos, Carsten Maple, Kay Atefi

机构 * University of Oxford(牛津大学) Cisco Systems(思科系统) The Alan Turing Institute(艾伦·图灵研究所) University of Warwick – WMG(沃里克大学 – WMG) University of Hull(哈罗德大学)

AI总结 提出一种协议驱动框架,通过绑定SBOM和AIBOM工件与确定性环境捕获及结构化运行时遥测,结合静态与运行时证据生成CSAF VEX公告,经密码签名和确定性重放验证,在合成自主AI工作负载上评估。

Journal ref Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework. Front Artif Intell 9, (May 2026), 1826384

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2606.19539 2026-06-19 astro-ph.SR cs.AI 新提交

Review of Machine Learning Models for Solar Energetic Particle Prediction

太阳高能粒子预测的机器学习模型综述

Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman, Ricky Egeland, Manolis Georgoulis, Angelos Vourlidas, Athanasios Papaioannou, Eleni Lavasa, Anastasios Anastasiadis, Giorgos Giannopoulos, Andres Munoz-Jaramillo, Bala Poduval, Irina N. Kitiashvili, Alexander G. Kosovichev, Viacheslav Sadykov, Soukaina Filali Boubrahimi, Tate T. Hutchins, Hameedullah A. Farooki, Manuel E. Cuesta, Leng Y. Khoo, Sungmin Pak, Robert Czarnota, Jamie S. Rankin, Jamey Szalay, Mitchell M. Shen, Georgios Livadiotis, Zigong Xu, David J. McComas, Nikolaos Sarlis, Dionissios Hristopulos, Arik Posner, Alec J. Engell, Mohammed AbuBakr Ali, Ali G. A. Abdelkawy, Abdelrazek M. K. Shaltout, M. M. Beheary, Christina O. Lee, Sigiava Aminalragia-Giamini, Constantinos Papadimitriou, Ingmar Sandberg, Savvas Raptis, Shah Muhammad Hamdi, Monica Laurenza, Mirko Stumpo, Sumanth A. Rotti, India Jackson, Aatiya Ali, Atilim Gunes Baydin, Nathan Schwadron, Subhamoy Chatterjee, Maher A. Dayeh, Gelu M. Nita, Patrick M. O'Keefe, Chun Jie Chong, Paul Kosovich, Russell D. Marroquin, Berkay Aydin, Petrus C. Martens, Lulu Zhao, Yang Chen, Yian Yu, Monica G. Bobra, Ward Manchester, Tamas Gombosi, Ming Zhang, Jesse Torres, Philip K. Chan, Mohamed Nedal, Kamen Kozarev, Peijin Zhang, Kimberly Moreland, Hazel M. Bain, Samuel Hart, Michael J. Starkey, Alan G. Ling, Simone Benella

机构 * Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA Computational Physics Branch, NASA Ames Research Center, Moffett Field, CA, USA Department of Computer Science, Utah State University, Logan, UT, USA Space Radiation Analysis Group, NASA Johnson Space Center, Houston, TX, USA Johns Hopkins Applied Physics Lab, 11100 Johns Hopkins Rd, Laurel, MD 20723, United States Research Center for Astronomy Applied Mathematics of the Academy of Athens, 4 Soranou Efesiou Street, Athens 11527, Greece Institute for Astronomy, Astrophysics, Space Applications Southwest Research Institute, Boulder, CO, USA Space Science Center, University of New Hampshire, Durham, NH, USA Department of Physics, New Jersey Institute of Technology, Newark, NJ, USA Astronomy Department, Georgia State University, Atlanta, GA, USA Department of Computer Science, Princeton University, Princeton, NJ, USA Department of Mathematics, Rowan University, Glassboro, NJ, USA Astronomy, California Institute of Technology, Pasadena, CA, USA Department of Physics, National Kapodistrian University of Athens, Athens, Greece School of Electrical Computer Engineering, Technical University of Crete, Chania, Greece Department of Astronomy Meteorology, Faculty of Science, Al-Azhar University, Cairo, Egypt Space Sciences Lab, University of California, Berkeley, CA, USA Research Consultancy, Athens, Greece Institute for Space Astrophysics Department of Physics Astronomy, Georgia State University, Atlanta, GA 30303, USA Aryabhatta Research Institute of Observational Sciences (ARIES), Manora Peak, Nainital-263001, Uttarakhand, India Department of Computer Science, Oxford University, Oxford, England Southwest Research Institute, San Antonio, TX, USA Computer Science Department, New Jersey Institute of Technology, Newark, NJ, USA Department of Physics, University of California San Diego, La Jolla, CA 92093, USA Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA Department of Climate Engineering, University of Michigan, Ann Arbor, MI, USA Department of Statistics, University of Michigan, Ann Arbor, MI, USA Department of Electrical Engineering Computer Science, Florida Institute of Technology, Melbourne, FL, USA Astrophysics Section, School of Cosmic Physics, Dublin Institute for Advanced Studies, DIAS Dunsink Observatory, Dublin D15 XR2R, Ireland Institute of Astronomy of the Bulgarian Academy of Sciences, Sofia, Bulgaria Center for Solar-Terrestrial Research, New Jersey Institute of Technology, Newark, NJ 07102, USA Cooperative Programs for the Advancement of Earth System Science, University Corporation for Atmospheric Research, Boulder, CO, USA CIRES, University of Colorado Boulder, Boulder, CO, USA Space Weather Prediction Center, NOAA, Boulder, CO, USA Astronomy, College of Science, The University of Texas at San Antonio, San Antonio, TX, USA Space Weather Prediction Center, National Oceanic The University of Texas at San Antonio, San Antonio, TX, USA Environmental Research, Inc., MA, USA

AI总结 综述了用于太阳高能粒子预测的机器学习模型,包括数据集、架构、输入输出比较,并提出了未来研究建议。

Comments Review Paper, Maine text: 23 pages, References: 5 pages, Appendix: 42 pages

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2606.15015 2026-06-19 cs.CV cs.AI 新提交

NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics

NEXUS: 用于物理一致的高接触3D物体动力学的神经能量场

Qizhen Ying, Guangming Wang, Yangchen Pan, Victor Adrian Prisacariu, Brian Sheil, Yixiong Jing

机构 * University of Oxford(牛津大学) University of Cambridge(剑桥大学)

AI总结 提出神经能量场框架NEXUS,通过标量能量和耗散项建模保守与非保守动力学,提升高接触3D场景下的长时程轨迹精度并指导视频生成。

Comments 18 pages, 4 figures, 6 tables. Preprint

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2601.14430 2026-06-19 stat.ML cs.LG 版本更新

Meta Flow Maps enable scalable reward alignment

元流映射实现可扩展的奖励对齐

Peter Potaptchik, Adhi Saravanan, Abbas Mammadov, Alvaro Prat, Michael S. Albergo, Yee Whye Teh

机构 * University of Oxford(牛津大学) Harvard University(哈佛大学) Kempner Institute(凯普纳研究所)

AI总结 提出元流映射(MFMs)框架,通过可微分的单步后验采样实现高效价值函数估计,从而无需轨迹模拟即可进行推理时引导和离策略微调,显著降低计算成本。

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2503.04507 2026-06-19 q-bio.QM cs.CG cs.LG 交叉投稿

The Morse Transform for Discrete Shape Analysis

离散形状分析的Morse变换

Alexander M. Tanaka, Aras T. Asaad, Richard Cooper, Vidit Nanda

机构 * Mathematical Institute, University of Oxford(牛津大学数学研究所) Oxford Drug Design Ltd(牛津药物设计有限公司) Oxford Centre for Innovation(牛津创新中心) Inorganic Chemistry Laboratory, University of Oxford(牛津大学无机化学实验室)

AI总结 提出一种基于定向分段线性Morse理论的拓扑变换,通过记录多个高度函数下的临界点来量化嵌入对象的几何形状,生成的特征向量在配体虚拟筛选中取得最优平均AUROC。

Comments 37 pages, 3 main figures, 2 main tables, 12 appendix figures and 4 appendix tables

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2501.18322 2026-06-19 cs.LG math.AP 版本更新

A Unified Perspective on the Dynamics of Deep Transformers

深度Transformer动力学的统一视角

Valérie Castin, Pierre Ablin, José Antonio Carrillo, Gabriel Peyré

机构 * CNRS and Ecole Normale Supérieure PSL(CNRS和巴黎高等师范大学) Apple(苹果公司) Mathematical Institute, University of Oxford(牛津大学数学学院)

AI总结 提出Transformer PDE作为注意力层迭代的均场极限,证明其适定性并分析高斯初始数据下的各向异性演化与聚类现象。

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2606.19156 2026-06-18 cs.CV 新提交

Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos

Hand-4DGS: 用于从第一人称视频进行4D手部重建的前馈3D高斯泼溅方法

Jeongmin Bae, Seoha Kim, Marc Pollefeys, Mahdi Rad, Youngjung Uh, Taein Kwon

机构 * Yonsei University(延世大学) Electronics and Telecommunications Research Institute(电子电信研究院) ETH Zurich(苏黎世联邦理工学院) Microsoft Spatial AI Lab(微软空间AI实验室) VGG, University of Oxford(VGG,牛津大学)

AI总结 提出Hand-4DGS,首个前馈框架,从第一人称视频直接重建动态4D手部,利用网格引导表示和时间卷积,实现快速推理和强泛化,无需3D真值标注。

Comments Project page: https://jeongminb.github.io/hand-4dgs/

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2606.18650 2026-06-18 cs.LG 新提交

BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training

BLADE: 面向LLM训练的可扩展双层自适应数据选择

Jiaxing Wang, Deping Xiang, Jin Xu, Zirui Liu, Zicheng Zhang, Guoqiang Gong, Jun Fang, Chao Liu, Pengzhang Liu, Tongxuan Liu, Ke Zhang, Qixia Jiang

机构 * University of Oxford(牛津大学) Renmin University of China(中国人民大学) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 提出BLADE框架,通过拉格朗日乘子将双层优化转化为单层惩罚目标,避免逆Hessian计算,实现动态参考模型,理论保证一阶收敛,实验优于现有方法。

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2606.18338 2026-06-18 cs.LG astro-ph.EP astro-ph.IM 新提交

ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets

ThousandWorlds: 一个用于潜在宜居系外行星气候模拟的基准数据集

Edward T. Stevenson, Mei Ting Mak, Eric Wolf, Denis E. Sergeev, Tobi Hammond, N. J. Mayne, Miles Cranmer

机构 * University of Cambridge(剑桥大学) University of Oxford(牛津大学) University of Colorado Boulder(科罗拉多大学博尔德分校) University of Bristol(布里斯托大学) Purdue University(普渡大学) University of Exeter(埃克塞特大学)

AI总结 为加速系外行星气候模拟,提出ThousandWorlds基准数据集,包含五个全球气候模型的约1800次模拟,用于评估机器学习模拟器在低数据、多模拟器参数到场回归任务中的性能。

Comments 10 pages main text, 26 pages references/appendix, plus NeurIPS checklist. Data at https://doi.org/10.57967/hf/8695. Code at https://github.com/edstevenson/ThousandWorlds

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2606.18464 2026-06-18 astro-ph.IM astro-ph.EP cs.LG 新提交

Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection

利用深度学习建模径向速度数据中的多普勒频移以探测地球质量系外行星

Isidro Gómez-Vargas, Xavier Dumusque, Yinan Zhao, Khaled Al Moulla, Michael Cretignier

机构 * Department of Astronomy, University of Geneva 51 chemin de Pegasi, 1290 Versoix, Switzerland. Instituto de Astrofı\'isica de Andaluc\'ia (CSIC), Glorieta de la Astronom\'ia s/n, E-18008 Granada, Spain. Institute of Space Sciences (CSIC), Carrer de Can Magrans s/n, E-08193 Barcelona, Spain. Department of Astronomy, University of Texas at Austin, 2515 Speedway, Austin, TX 78712, USA. Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal. Department of Physics, University of Oxford, OX13RH Oxford, UK.

AI总结 针对恒星活动干扰,提出结合物理启发光谱表示与深度学习的框架,通过交叉验证和遗传算法优化,可靠恢复振幅≥25 cm/s、周期10-550天的行星信号,并发布Python包doppleriann。

Comments 20 pages, 14 figures. Accepted for publication in Astronomy & Astrophysics

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2606.11918 2026-06-18 cs.AI 新提交

The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning

提问的艺术:一致性增强空间推理中的事实性

Theo Uscidda, Marta Tintore Gazulla, Maks Ovsjanikov, Federico Tombari, Leonidas Guibas

机构 * The University of California, Berkeley(加州大学伯克利分校) ETH Zurich(苏黎世联邦理工学院) University of Oxford(牛津大学) Stanford University(斯坦福大学)

AI总结 提出自监督强化学习框架,通过几何与语义一致性验证器(如图像翻转、文本对象顺序交换)对齐预训练模型的内在空间推理能力,无需标注数据即可达到接近监督方法的精度。

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2606.01249 2026-06-18 cs.LG cs.CL 版本更新

Trust Region On-Policy Distillation

信任区域在线策略蒸馏

Xingrun Xing, Haoqing Wang, Boyan Gao, Ziheng Li, Yehui Tang

机构 * Samsung Research(三星研究院) University of Oxford(牛津大学) Peking University(北京大学)

AI总结 提出信任区域在线策略蒸馏(TrOPD),通过信用分配策略和信任区域学习解决师生分布差异导致的训练不稳定问题,在数学推理、代码生成和通用基准上超越现有方法。

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2510.21605 2026-06-18 cs.CV 版本更新

S3OD: Towards Generalizable Salient Object Detection with Synthetic Data

S3OD:基于合成数据的通用显著目标检测

Orest Kupyn, Hirokatsu Kataoka, Christian Rupprecht

机构 * University of Oxford, VGG(牛津大学,视觉信息集团)

AI总结 提出S3OD方法,通过大规模合成数据生成和歧义感知架构,显著提升显著目标检测的跨数据集泛化能力,仅用合成数据训练即可降低20-50%误差。

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2511.05221 2026-06-18 cs.LG q-bio.NC 版本更新

ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy

ActiTect:通过标准化体动记录进行REM睡眠行为障碍筛查的通用机器学习流程

David Bertram, Anja Ophey, Sinah Röttgen, Konstantin Kufer, Gereon R. Fink, Elke Kalbe, Clint Hansen, Walter Maetzler, Maximilian Kapsecker, Lara M. Reimer, Stephan Jonas, Andreas T. Damgaard, Natasha B. Bertelsen, Casper Skjaerbaek, Per Borghammer, Karolien Groenewald, Pietro-Luca Ratti, Michele T. Hu, Noémie Moreau, Michael Sommerauer, Katarzyna Bozek

机构 * Faculty of Mathematics and Natural Sciences, University of Cologne, Germany(科隆大学数学与自然科学学院,德国) Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany(科隆大学医学院与科隆大学医院生物医学信息学研究所,德国) Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany(科隆分子医学中心(CMMC),科隆大学医学院与科隆大学医院,德国) Medical Psychology | Neuropsychology and Gender Studies, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany(科隆大学医学院与科隆大学医院医学心理学 | 神经心理学与性别研究,德国) Cognitive Neuroscience, Insitute for Neuroscience and Medicine, INM-3, Research Center Juelich, Germany(认知神经科学,神经科学与医学研究所,Juelich研究中心,德国) Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany(科隆大学医学院与科隆大学医院神经科,德国) Center of Neurology, Department of Parkinson, Sleep and Movement Disorders, University Hospital Bonn, University of Bonn, Germany(神经科中心,帕金森、睡眠与运动障碍部门,波恩大学医院,德国) German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany(德国神经退行性疾病研究中心(DZNE),波恩,德国) Cluster of Excellence for Aging and Aging-Associated Diseases (CECAD), University of Cologne, Germany(老龄化与相关疾病卓越中心(CECAD),科隆大学,德国) Department of Neurology, University Medical Center Schleswig-Holstein, Campus Kiel and Kiel University, Germany(神经科,施普伦德-霍斯特大学医院,基尔校区和基尔大学,德国) Department of Informatics, Technical University of Munich, Germany(信息学院,慕尼黑技术大学,德国) Institute for Digital Medicine, University Hospital Bonn, Germany(数字医学研究所,波恩大学医院,德国) Lundbeck Foundation Parkinson’s Disease Research Center (PACE), Aarhus University, Denmark(路德维希基金会帕金森病研究中心(PACE),奥胡斯大学,丹麦) Department of Nuclear Medicine, Aarhus University Hospital, Denmark(核医学部,奥胡斯大学医院,丹麦) Department of Electrical and Computer Engineering, Aarhus University, Denmark(电气与计算机工程系,奥胡斯大学,丹麦) Oxford Parkinson’s Disease Centre and Division of Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, UK(牛津帕金森病中心与神经科,牛津大学临床神经科学系,英国)

AI总结 提出ActiTect,一个全自动开源机器学习工具,通过标准化预处理和睡眠-觉醒检测,从体动记录中识别RBD,在多个独立队列中验证了泛化能力(AUROC 0.84-0.94)。

Comments 37 pages including Supplementary Information, 4 core figures, 1 supplementary figure. (v2: fixed a typo in Table 3 and made minor text edits; v3: post review)

Journal ref npj Digital Medicine (2026)

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2510.21615 2026-06-18 cs.CV 版本更新

Epipolar Geometry Improves Video Generation Models

极线几何改进视频生成模型

Orest Kupyn, Théo Uscidda, Marta Tintore Gazulla, Fabian Manhardt, Federico Tombari, Christian Rupprecht

机构 * University of Oxford(牛津大学) Google Research(谷歌研究院) CREST-ENSAE, Institut Polytechnique de Paris(巴黎理工学院CREST-ENSAE研究中心) Technical University of Munich(慕尼黑技术大学)

AI总结 针对视频生成模型几何不一致和运动伪影问题,提出基于极线几何约束的偏好优化方法,在保持视觉质量的同时将极线误差降低31%,人类评分一致性从54%提升至72%。

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2504.04739 2026-06-18 cs.LG cs.CY 版本更新

UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction

UST-GNN:面向城市分析的空间-拓扑统一图神经网络框架——以城市健康预测为例

Minwei Zhao, Sanja Scepanovic, Stephen Law, Ivica Obadic, Cai Wu, Daniele Quercia

机构 * University College London(伦敦大学学院) The Hong Kong University of Science(香港科学大学) Nokia Bell Labs(诺基亚贝尔实验室) Technical University of Munich(慕尼黑技术大学) University of Oxford(牛津大学)

AI总结 提出UST-GNN框架,整合邻域连通性、异质城市特征和位置嵌入,在大伦敦4835个邻域的健康预测中,严格空间交叉验证下R²提升8.4-13.2%,并引入主成分模块解释嵌入。

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2407.18245 2026-06-18 cs.CV cs.LG 版本更新

VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset

VGGHeads: 基于大规模合成数据集的3D多头部对齐

Orest Kupyn, Eugene Khvedchenia, Christian Rupprecht

机构 * University of Oxford(牛津大学) Piñata Farms Ukrainian Catholic University(乌克兰天主大学)

AI总结 提出VGGHeads,一个由扩散模型生成的大规模合成数据集,用于单步同时进行头部检测和3D网格重建,在真实图像上表现优异。

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2606.17065 2026-06-17 q-fin.CP cs.AI cs.LG 新提交

PIVOT: Bridging Black-Scholes Implied-Volatility and Price Objectives via Differentiable Jäckel Operator

PIVOT: 通过可微分的Jäckel算子桥接Black-Scholes隐含波动率与价格目标

Raeid Saqur, Yannick Limmer, Anastasis Kratsios, Blanka Horvath, Hans Buehler

机构 * Mathematical Institute, University of Oxford(牛津大学数学研究所) McMaster University(麦基尔大学) Vector Institute for AI(人工智能矢量研究所) DRW

AI总结 提出PIVOT层,通过隐式微分保留Jäckel求解器的前向精度,并利用门控机制处理低vega区域的奇异性,实现价格与隐含波动率空间的高效可微转换。

Comments 30 pages, 17 figures, 12 tables

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2606.17286 2026-06-17 cs.CY cs.AI 新提交

From Democracies to Autocracies: How AI Systems Enable Authoritarianism by Design

从民主到专制:AI系统如何通过设计实现威权主义

Jeba Sania, Marta Ziosi, Fazl Barez

机构 * Harvard Kennedy School(哈佛肯尼迪学校) University of Oxford(牛津大学)

AI总结 本文通过比较美国到中国的六种AI系统生命周期,识别出集中行政数据、监管漏洞、弱用户合规性及编码受保护群体特征等关键特征,揭示AI系统在不同政体中促成威权主义的机制。

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2606.17530 2026-06-17 physics.soc-ph cs.LG econ.GN q-fin.EC stat.AP 新提交

Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

纽约市拥堵收费后公共交通增益与空间不均的出行需求变化

Donghang Li, Dingyi Zhuang, Yunlin Li, Chenan Shen, Nina Cao, Yunhan Zheng, Shenhao Wang, Jinhua Zhao

机构 * Department of Civil and Environmental Engineering, Massachusetts Institute of Technology(麻省理工学院土木与环境工程系) Department of Urban Studies and Planning, Massachusetts Institute of Technology(麻省理工学院城市研究与规划系) Mathematical Institute, University of Oxford(牛津大学数学院) Department of Mechanical Engineering, Massachusetts Institute of Technology(麻省理工学院机械工程系) College of Urban and Environmental Sciences, Peking University(北京大学城市与环境科学学院) Department of Urban and Regional Planning, University of Florida(佛罗里达大学城市与区域规划系) Center for Computational Science and Engineering, Massachusetts Institute of Technology(麻省理工学院计算科学与工程中心)

AI总结 利用时间序列基础模型生成概率反事实预测,评估纽约市2025年实施的拥堵收费政策,发现公交和地铁客流量显著增加,但总体出行需求略有下降,且影响存在空间异质性。

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2606.08402 2026-06-17 cs.CV cs.AI cs.MA 新提交

SceneConductor: 3D Scene Generation from a Single Image with Multi-Agent Orchestration

SceneConductor: 基于多智能体编排的单图像3D场景生成

Jeonghwan Kim, Yushi Lan, Yongwei Chen, Hieu Trung Nguyen, Chuanyu Pan, Xingang Pan

机构 * Nanyang Technological University(南洋理工大学) University of Oxford(牛津大学) Meshy AI

AI总结 提出多智能体编排框架,将单图像3D场景生成分解为场景初始化、环境构建和多智能体细化三个阶段,并引入几何感知布局预测器,在几何精度、空间一致性和感知真实性上超越现有方法。

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2602.14211 2026-06-17 cs.CR cs.AI 版本更新

SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

SkillJect:有效自动化基于技能的提示注入以针对具备技能的代理

Xiaojun Jia, Jie Liao, Simeng Qin, Jindong Gu, Wenqi Ren, Xiaochun Cao, Yang Liu, Philip Torr

机构 * Nanyang Technological University, Singapore(南洋理工大学,新加坡) Chongqing University, China(重庆大学) Northeastern University, China(东北大学) Sun Yat-sen University, China(中山大学) University of Oxford, UK(牛津大学)

AI总结 SkillJect 是首个自动化生成有效中毒技能的框架,通过隐藏恶意负载和重写指令通道,提升攻击效果,揭示可重用技能生态中的持久性攻击向量。

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2604.22748 2026-06-17 cs.AI 版本更新

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

代理世界建模:基础、能力、定律及更远

Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin, Lingdong Kong, Jize Zhang, Teng Tu, Weijian Ma, Ziqi Huang, Senqiao Yang, Wei Huang, Yeying Jin, Zhefan Rao, Jinhui Ye, Xinyu Lin, Xichen Zhang, Qisheng Hu, Shuai Yang, Leyang Shen, Wei Chow, Yifei Dong, Fengyi Wu, Quanyu Long, Bin Xia, Shaozuo Yu, Mingkang Zhu, Wenhu Zhang, Jiehui Huang, Haokun Gui, Runyi Li, Chenyu Tang, Dong Huang, Xuhang Chen, Rui Liu, Chengzu Li, Shiyi Du, Xu Huang, Haoxuan Che, Long Chen, Qifeng Chen, Wenya Wang, Wenxuan Zhang, Xiaojuan Qi, Yang Deng, Yanwei Li, Mike Zheng Shou, Zhi-Qi Cheng, See-Kiong Ng, Ziwei Liu, Philip Torr, Jiaya Jia

机构 * Hong Kong University of Science and Technology(香港科学与技术大学) National University of Singapore(新加坡国立大学) University of Oxford(牛津大学) Nanyang Technological University(南洋理工大学) Chinese University of Hong Kong(香港中文大学) University of Hong Kong(香港大学) University of Washington(华盛顿大学) University of Tokyo(东京大学) Carnegie Mellon University(卡内基梅隆大学) University of California, Berkeley(加州大学伯克利分校) University of Cambridge(剑桥大学) Singapore University of Technology and Design(新加坡科技设计大学) Singapore Management University(新加坡管理学院) XGEN Labs(XGEN实验室)

AI总结 本文提出'层次x定律'分类法,定义三个能力层级和四个约束领域,综合400余篇文献总结100余系统,分析方法、失败模式和评估实践,提出决策导向的评估原则和可复现评估包,展望从被动预测到重塑环境的代理世界建模路径。

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2603.04198 2026-06-17 stat.ML cs.LG 版本更新

Stable and Steerable Sparse Autoencoders with Weight Regularization

基于权重正则化的稳定且可操控的稀疏自编码器

Piotr Jedryszek, Oliver M. Crook

机构 * Department of Biology, University of Oxford, Oxford, UK(牛津大学生物学系) Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford, UK(牛津大学纳科学发现研究所) Department of Chemistry, University of Oxford, Oxford, UK(牛津大学化学系)

AI总结 通过L1/L2权重正则化提高稀疏自编码器的跨种子特征一致性,并在语言模型上提升操控成功率,同时保持可解释性分数。

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